CBTI Summary

Consort map

Demographic information

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

age

358

36.34 ± 13.94 (18 - 73)

35.95 ± 13.84 (18 - 73)

36.72 ± 14.07 (18 - 71)

0.599

gender

358

0.792

female

286 (80%)

142 (79%)

144 (80%)

male

72 (20%)

37 (21%)

35 (20%)

occupation

358

0.658

civil

13 (3.6%)

4 (2.2%)

9 (5.0%)

clerk

57 (16%)

30 (17%)

27 (15%)

craft

12 (3.4%)

8 (4.5%)

4 (2.2%)

homemaker

26 (7.3%)

14 (7.8%)

12 (6.7%)

manager

28 (7.8%)

16 (8.9%)

12 (6.7%)

other

15 (4.2%)

5 (2.8%)

10 (5.6%)

professional

39 (11%)

16 (8.9%)

23 (13%)

retired

21 (5.9%)

10 (5.6%)

11 (6.1%)

service

12 (3.4%)

7 (3.9%)

5 (2.8%)

student

119 (33%)

60 (34%)

59 (33%)

unemploy

16 (4.5%)

9 (5.0%)

7 (3.9%)

marital

358

0.652

divorced

14 (3.9%)

5 (2.8%)

9 (5.0%)

married

97 (27%)

51 (28%)

46 (26%)

other

2 (0.6%)

1 (0.6%)

1 (0.6%)

separated

5 (1.4%)

1 (0.6%)

4 (2.2%)

single

235 (66%)

119 (66%)

116 (65%)

widowed

5 (1.4%)

2 (1.1%)

3 (1.7%)

marital_r

358

0.252

married

97 (27%)

51 (28%)

46 (26%)

other

26 (7.3%)

9 (5.0%)

17 (9.5%)

single

235 (66%)

119 (66%)

116 (65%)

education

358

0.914

post-secondary

52 (15%)

28 (16%)

24 (13%)

primary

2 (0.6%)

1 (0.6%)

1 (0.6%)

secondary

50 (14%)

24 (13%)

26 (15%)

university

254 (71%)

126 (70%)

128 (72%)

education_r

358

0.819

post-secondary

52 (15%)

28 (16%)

24 (13%)

secondary or below

52 (15%)

25 (14%)

27 (15%)

university

254 (71%)

126 (70%)

128 (72%)

family_income

358

0.502

0_10000

56 (16%)

27 (15%)

29 (16%)

10001_20000

75 (21%)

38 (21%)

37 (21%)

20001_30000

73 (20%)

42 (23%)

31 (17%)

30001_40000

60 (17%)

31 (17%)

29 (16%)

40000_above

94 (26%)

41 (23%)

53 (30%)

religion

358

0.110

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

3 (0.8%)

0 (0%)

3 (1.7%)

taoism

1 (0.3%)

1 (0.6%)

0 (0%)

religion_r

358

0.234

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

4 (1.1%)

1 (0.6%)

3 (1.7%)

source

358

0.233

bokss

15 (4.2%)

11 (6.1%)

4 (2.2%)

facebook

131 (37%)

63 (35%)

68 (38%)

instagram

12 (3.4%)

7 (3.9%)

5 (2.8%)

other

66 (18%)

28 (16%)

38 (21%)

refresh

134 (37%)

70 (39%)

64 (36%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Table

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

isi

358

13.47 ± 3.37 (8 - 21)

13.53 ± 3.33 (8 - 21)

13.40 ± 3.42 (8 - 21)

0.719

who

358

9.90 ± 3.74 (0 - 21)

9.82 ± 3.71 (1 - 20)

9.98 ± 3.77 (0 - 21)

0.682

phq

358

8.51 ± 5.01 (0 - 25)

8.21 ± 4.98 (0 - 21)

8.80 ± 5.03 (0 - 25)

0.264

gad

358

7.78 ± 5.12 (0 - 21)

7.54 ± 5.03 (0 - 21)

8.02 ± 5.21 (0 - 21)

0.376

wsas

358

16.73 ± 9.85 (0 - 40)

16.77 ± 9.70 (0 - 39)

16.69 ± 10.03 (0 - 40)

0.936

shps_arousal

358

3.10 ± 0.69 (1 - 5)

3.02 ± 0.68 (1 - 5)

3.18 ± 0.69 (1 - 5)

0.025

shps_schedule

358

3.55 ± 0.87 (1 - 6)

3.53 ± 0.81 (2 - 6)

3.58 ± 0.93 (1 - 6)

0.653

shps_behavior

358

2.05 ± 0.66 (1 - 4)

1.99 ± 0.61 (1 - 4)

2.12 ± 0.71 (1 - 4)

0.059

shps_environment

358

2.30 ± 0.82 (1 - 5)

2.33 ± 0.84 (1 - 5)

2.27 ± 0.80 (1 - 5)

0.473

dbas_consequence

358

6.61 ± 1.75 (1 - 10)

6.59 ± 1.82 (1 - 10)

6.64 ± 1.68 (1 - 10)

0.772

dbas_worry

358

14.37 ± 3.23 (3 - 20)

14.20 ± 3.35 (3 - 20)

14.54 ± 3.11 (3 - 20)

0.319

dbas_expectation

358

7.03 ± 2.14 (1 - 10)

7.17 ± 2.09 (1 - 10)

6.89 ± 2.19 (1 - 10)

0.209

dbas_medication

358

3.19 ± 2.07 (0 - 9)

3.15 ± 2.04 (0 - 9)

3.24 ± 2.09 (0 - 9)

0.683

psas_somatic

358

1.88 ± 0.69 (1 - 5)

1.86 ± 0.66 (1 - 4)

1.91 ± 0.71 (1 - 5)

0.539

psas_cognitive

358

2.92 ± 0.85 (1 - 5)

2.87 ± 0.84 (1 - 5)

2.97 ± 0.86 (1 - 5)

0.270

psqi_global

358

10.87 ± 3.02 (2 - 19)

10.72 ± 3.03 (4 - 17)

11.01 ± 3.00 (2 - 19)

0.363

mic_attention

358

1.36 ± 0.72 (0 - 3)

1.30 ± 0.71 (0 - 3)

1.42 ± 0.73 (0 - 3)

0.110

mic_executive

358

1.31 ± 0.76 (0 - 3)

1.28 ± 0.77 (0 - 3)

1.35 ± 0.76 (0 - 3)

0.406

mic_memory

358

1.37 ± 0.73 (0 - 3)

1.33 ± 0.75 (0 - 3)

1.40 ± 0.71 (0 - 3)

0.397

nb_pcs

358

46.27 ± 8.63 (17 - 65)

46.33 ± 8.91 (17 - 63)

46.20 ± 8.38 (21 - 65)

0.879

nb_mcs

358

39.94 ± 9.95 (8 - 65)

39.90 ± 9.78 (8 - 62)

39.98 ± 10.14 (8 - 65)

0.935

1Mean ± SD (Range)

2Two Sample t-test

Plot

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

isi

(Intercept)

13.5

0.286

13.0, 14.1

group

control

—

—

—

treatment

-0.128

0.404

-0.920, 0.663

0.751

time_point

1st

—

—

—

2nd

-2.46

0.323

-3.09, -1.82

0.000

3rd

-2.84

0.331

-3.49, -2.19

0.000

group * time_point

treatment * 2nd

-2.96

0.486

-3.92, -2.01

0.000

treatment * 3rd

-2.99

0.496

-3.97, -2.02

0.000

Pseudo R square

0.260

who

(Intercept)

9.82

0.305

9.22, 10.4

group

control

—

—

—

treatment

0.162

0.432

-0.684, 1.01

0.708

time_point

1st

—

—

—

2nd

0.729

0.299

0.143, 1.32

0.015

3rd

0.925

0.307

0.323, 1.53

0.003

group * time_point

treatment * 2nd

1.40

0.453

0.514, 2.29

0.002

treatment * 3rd

1.63

0.462

0.728, 2.54

0.000

Pseudo R square

0.053

phq

(Intercept)

8.21

0.379

7.47, 8.95

group

control

—

—

—

treatment

0.592

0.535

-0.457, 1.64

0.269

time_point

1st

—

—

—

2nd

-0.779

0.333

-1.43, -0.126

0.020

3rd

-0.642

0.343

-1.31, 0.029

0.061

group * time_point

treatment * 2nd

-1.73

0.506

-2.73, -0.742

0.001

treatment * 3rd

-2.44

0.516

-3.45, -1.43

0.000

Pseudo R square

0.039

gad

(Intercept)

7.54

0.382

6.79, 8.28

group

control

—

—

—

treatment

0.480

0.540

-0.577, 1.54

0.374

time_point

1st

—

—

—

2nd

-0.440

0.341

-1.11, 0.229

0.198

3rd

-0.585

0.351

-1.27, 0.102

0.096

group * time_point

treatment * 2nd

-2.07

0.518

-3.09, -1.06

0.000

treatment * 3rd

-2.38

0.528

-3.41, -1.34

0.000

Pseudo R square

0.038

wsas

(Intercept)

16.8

0.749

15.3, 18.2

group

control

—

—

—

treatment

-0.084

1.059

-2.16, 1.99

0.937

time_point

1st

—

—

—

2nd

-0.819

0.695

-2.18, 0.543

0.239

3rd

-0.116

0.715

-1.52, 1.28

0.871

group * time_point

treatment * 2nd

-2.95

1.054

-5.02, -0.886

0.005

treatment * 3rd

-4.92

1.076

-7.03, -2.81

0.000

Pseudo R square

0.034

shps_arousal

(Intercept)

3.02

0.055

2.91, 3.13

group

control

—

—

—

treatment

0.163

0.078

0.009, 0.316

0.039

time_point

1st

—

—

—

2nd

-0.196

0.059

-0.312, -0.079

0.001

3rd

-0.219

0.061

-0.339, -0.100

0.000

group * time_point

treatment * 2nd

-0.477

0.089

-0.653, -0.302

0.000

treatment * 3rd

-0.564

0.091

-0.743, -0.386

0.000

Pseudo R square

0.112

shps_schedule

(Intercept)

3.53

0.067

3.40, 3.66

group

control

—

—

—

treatment

0.042

0.094

-0.143, 0.226

0.659

time_point

1st

—

—

—

2nd

-0.101

0.060

-0.218, 0.017

0.094

3rd

-0.133

0.062

-0.254, -0.013

0.031

group * time_point

treatment * 2nd

-0.345

0.091

-0.523, -0.167

0.000

treatment * 3rd

-0.424

0.093

-0.606, -0.242

0.000

Pseudo R square

0.045

shps_behavior

(Intercept)

1.99

0.051

1.89, 2.08

group

control

—

—

—

treatment

0.132

0.072

-0.009, 0.273

0.067

time_point

1st

—

—

—

2nd

0.024

0.051

-0.075, 0.124

0.629

3rd

0.012

0.052

-0.090, 0.114

0.816

group * time_point

treatment * 2nd

-0.244

0.077

-0.394, -0.094

0.002

treatment * 3rd

-0.336

0.078

-0.489, -0.182

0.000

Pseudo R square

0.020

shps_environment

(Intercept)

2.33

0.061

2.21, 2.45

group

control

—

—

—

treatment

-0.062

0.086

-0.230, 0.106

0.469

time_point

1st

—

—

—

2nd

-0.058

0.060

-0.176, 0.059

0.331

3rd

-0.060

0.062

-0.180, 0.061

0.334

group * time_point

treatment * 2nd

-0.085

0.091

-0.263, 0.092

0.347

treatment * 3rd

-0.259

0.093

-0.441, -0.078

0.005

Pseudo R square

0.021

dbas_consequence

(Intercept)

6.59

0.140

6.31, 6.86

group

control

—

—

—

treatment

0.054

0.199

-0.336, 0.443

0.787

time_point

1st

—

—

—

2nd

-0.336

0.141

-0.612, -0.061

0.017

3rd

-0.659

0.145

-0.942, -0.375

0.000

group * time_point

treatment * 2nd

-1.11

0.213

-1.53, -0.693

0.000

treatment * 3rd

-1.31

0.217

-1.74, -0.884

0.000

Pseudo R square

0.117

dbas_worry

(Intercept)

14.2

0.284

13.6, 14.8

group

control

—

—

—

treatment

0.341

0.401

-0.445, 1.13

0.396

time_point

1st

—

—

—

2nd

-1.23

0.323

-1.86, -0.598

0.000

3rd

-1.82

0.332

-2.47, -1.17

0.000

group * time_point

treatment * 2nd

-2.71

0.487

-3.67, -1.76

0.000

treatment * 3rd

-2.89

0.496

-3.86, -1.92

0.000

Pseudo R square

0.162

dbas_expectation

(Intercept)

7.17

0.172

6.84, 7.51

group

control

—

—

—

treatment

-0.285

0.244

-0.763, 0.193

0.243

time_point

1st

—

—

—

2nd

-0.343

0.176

-0.688, 0.002

0.052

3rd

-0.766

0.181

-1.12, -0.411

0.000

group * time_point

treatment * 2nd

-1.25

0.266

-1.77, -0.727

0.000

treatment * 3rd

-1.29

0.271

-1.82, -0.758

0.000

Pseudo R square

0.111

dbas_medication

(Intercept)

3.15

0.161

2.83, 3.46

group

control

—

—

—

treatment

0.089

0.228

-0.357, 0.536

0.695

time_point

1st

—

—

—

2nd

0.366

0.164

0.044, 0.688

0.026

3rd

0.306

0.169

-0.025, 0.637

0.071

group * time_point

treatment * 2nd

-0.664

0.249

-1.15, -0.177

0.008

treatment * 3rd

-0.857

0.254

-1.35, -0.360

0.001

Pseudo R square

0.015

psas_somatic

(Intercept)

1.86

0.051

1.76, 1.96

group

control

—

—

—

treatment

0.045

0.072

-0.096, 0.185

0.533

time_point

1st

—

—

—

2nd

0.143

0.047

0.051, 0.236

0.003

3rd

0.010

0.049

-0.086, 0.105

0.838

group * time_point

treatment * 2nd

-0.306

0.072

-0.447, -0.165

0.000

treatment * 3rd

-0.242

0.073

-0.386, -0.098

0.001

Pseudo R square

0.021

psas_cognitive

(Intercept)

2.87

0.063

2.75, 3.00

group

control

—

—

—

treatment

0.099

0.090

-0.077, 0.275

0.270

time_point

1st

—

—

—

2nd

-0.204

0.064

-0.329, -0.079

0.001

3rd

-0.359

0.066

-0.487, -0.230

0.000

group * time_point

treatment * 2nd

-0.434

0.097

-0.623, -0.245

0.000

treatment * 3rd

-0.411

0.099

-0.604, -0.218

0.000

Pseudo R square

0.091

psqi_global

(Intercept)

10.7

0.237

10.3, 11.2

group

control

—

—

—

treatment

0.291

0.335

-0.366, 0.947

0.386

time_point

1st

—

—

—

2nd

-1.31

0.258

-1.82, -0.808

0.000

3rd

-1.32

0.265

-1.83, -0.796

0.000

group * time_point

treatment * 2nd

-1.86

0.389

-2.62, -1.10

0.000

treatment * 3rd

-2.44

0.397

-3.22, -1.67

0.000

Pseudo R square

0.149

mic_attention

(Intercept)

1.30

0.057

1.19, 1.41

group

control

—

—

—

treatment

0.122

0.080

-0.035, 0.278

0.130

time_point

1st

—

—

—

2nd

-0.022

0.055

-0.131, 0.087

0.694

3rd

0.031

0.057

-0.081, 0.143

0.589

group * time_point

treatment * 2nd

-0.248

0.084

-0.412, -0.083

0.003

treatment * 3rd

-0.384

0.086

-0.552, -0.216

0.000

Pseudo R square

0.021

mic_executive

(Intercept)

1.28

0.058

1.17, 1.39

group

control

—

—

—

treatment

0.067

0.082

-0.094, 0.228

0.415

time_point

1st

—

—

—

2nd

-0.034

0.054

-0.140, 0.073

0.537

3rd

-0.051

0.056

-0.160, 0.059

0.365

group * time_point

treatment * 2nd

-0.159

0.083

-0.321, 0.002

0.054

treatment * 3rd

-0.270

0.084

-0.435, -0.105

0.001

Pseudo R square

0.015

mic_memory

(Intercept)

1.33

0.057

1.22, 1.44

group

control

—

—

—

treatment

0.066

0.081

-0.093, 0.224

0.417

time_point

1st

—

—

—

2nd

0.031

0.051

-0.069, 0.132

0.538

3rd

-0.062

0.053

-0.165, 0.041

0.235

group * time_point

treatment * 2nd

-0.276

0.078

-0.428, -0.124

0.000

treatment * 3rd

-0.221

0.079

-0.376, -0.066

0.005

Pseudo R square

0.017

nb_pcs

(Intercept)

46.3

0.658

45.0, 47.6

group

control

—

—

—

treatment

-0.139

0.931

-1.96, 1.69

0.882

time_point

1st

—

—

—

2nd

-0.871

0.590

-2.03, 0.285

0.140

3rd

-0.784

0.607

-1.97, 0.405

0.197

group * time_point

treatment * 2nd

2.76

0.896

1.00, 4.51

0.002

treatment * 3rd

3.20

0.914

1.41, 4.99

0.001

Pseudo R square

0.015

nb_mcs

(Intercept)

39.9

0.771

38.4, 41.4

group

control

—

—

—

treatment

0.085

1.090

-2.05, 2.22

0.938

time_point

1st

—

—

—

2nd

2.00

0.739

0.554, 3.45

0.007

3rd

2.27

0.760

0.781, 3.76

0.003

group * time_point

treatment * 2nd

3.57

1.121

1.37, 5.77

0.002

treatment * 3rd

4.66

1.144

2.42, 6.90

0.000

Pseudo R square

0.056

1SE = Standard Error, CI = Confidence Interval

Text

isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.53 (95% CI [12.97, 14.09], t(849) = 47.37, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(849) = -0.32, p = 0.750; Std. beta = -0.03, 95% CI [-0.21, 0.15])
  • The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.82], t(849) = -7.62, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
  • The effect of time point [3rd] is statistically significant and negative (beta = -2.84, 95% CI [-3.49, -2.19], t(849) = -8.57, p < .001; Std. beta = -0.63, 95% CI [-0.78, -0.49])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.92, -2.01], t(849) = -6.09, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.99, 95% CI [-3.97, -2.02], t(849) = -6.04, p < .001; Std. beta = -0.67, 95% CI [-0.88, -0.45])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 9.82 (95% CI [9.22, 10.42], t(849) = 32.17, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.68, 1.01], t(849) = 0.38, p = 0.708; Std. beta = 0.04, 95% CI [-0.16, 0.24])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.73, 95% CI [0.14, 1.32], t(849) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
  • The effect of time point [3rd] is statistically significant and positive (beta = 0.93, 95% CI [0.32, 1.53], t(849) = 3.01, p = 0.003; Std. beta = 0.22, 95% CI [0.08, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.40, 95% CI [0.51, 2.29], t(849) = 3.10, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 1.63, 95% CI [0.73, 2.54], t(849) = 3.53, p < .001; Std. beta = 0.39, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 8.21 (95% CI [7.47, 8.95], t(849) = 21.69, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(849) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(849) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.64, 95% CI [-1.31, 0.03], t(849) = -1.88, p = 0.061; Std. beta = -0.12, 95% CI [-0.25, 5.60e-03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(849) = -3.43, p < .001; Std. beta = -0.33, 95% CI [-0.52, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.45, -1.43], t(849) = -4.73, p < .001; Std. beta = -0.47, 95% CI [-0.67, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.54 (95% CI [6.79, 8.28], t(849) = 19.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(849) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(849) = -1.29, p = 0.197; Std. beta = -0.08, 95% CI [-0.21, 0.04])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.59, 95% CI [-1.27, 0.10], t(849) = -1.67, p = 0.095; Std. beta = -0.11, 95% CI [-0.25, 0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(849) = -4.00, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.38, 95% CI [-3.41, -1.34], t(849) = -4.50, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 16.77 (95% CI [15.30, 18.24], t(849) = 22.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(849) = -0.08, p = 0.937; Std. beta = -8.24e-03, 95% CI [-0.21, 0.20])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(849) = -1.18, p = 0.239; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.12, 95% CI [-1.52, 1.28], t(849) = -0.16, p = 0.871; Std. beta = -0.01, 95% CI [-0.15, 0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.89], t(849) = -2.80, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.92, 95% CI [-7.03, -2.81], t(849) = -4.57, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.02 (95% CI [2.91, 3.13], t(849) = 54.48, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.89e-03, 0.32], t(849) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(849) = -3.30, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(849) = -3.61, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(849) = -5.34, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.39], t(849) = -6.19, p < .001; Std. beta = -0.71, 95% CI [-0.94, -0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.53 (95% CI [3.40, 3.66], t(849) = 53.13, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(849) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(849) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.13, 95% CI [-0.25, -0.01], t(849) = -2.17, p = 0.030; Std. beta = -0.15, 95% CI [-0.28, -0.01])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(849) = -3.79, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.61, -0.24], t(849) = -4.56, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.99 (95% CI [1.89, 2.08], t(849) = 39.01, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.85e-03, 0.27], t(849) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(849) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.01, 95% CI [-0.09, 0.11], t(849) = 0.23, p = 0.816; Std. beta = 0.02, 95% CI [-0.13, 0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(849) = -3.18, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.49, -0.18], t(849) = -4.29, p < .001; Std. beta = -0.49, 95% CI [-0.71, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.33 (95% CI [2.21, 2.45], t(849) = 38.45, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(849) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(849) = -0.97, p = 0.330; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(849) = -0.97, p = 0.333; Std. beta = -0.07, 95% CI [-0.22, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(849) = -0.94, p = 0.346; Std. beta = -0.10, 95% CI [-0.32, 0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(849) = -2.80, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st, is at 6.59 (95% CI [6.31, 6.86], t(849) = 46.92, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(849) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(849) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.66, 95% CI [-0.94, -0.38], t(849) = -4.56, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(849) = -5.22, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.31, 95% CI [-1.74, -0.88], t(849) = -6.03, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.20 (95% CI [13.65, 14.76], t(849) = 50.09, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.45, 1.13], t(849) = 0.85, p = 0.395; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(849) = -3.81, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.82, 95% CI [-2.47, -1.17], t(849) = -5.50, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.28])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.67, -1.76], t(849) = -5.57, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.89, 95% CI [-3.86, -1.92], t(849) = -5.82, p < .001; Std. beta = -0.70, 95% CI [-0.93, -0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.17 (95% CI [6.84, 7.51], t(849) = 41.63, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(849) = -1.17, p = 0.242; Std. beta = -0.12, 95% CI [-0.31, 0.08])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.61e-03], t(849) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 6.59e-04])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.77, 95% CI [-1.12, -0.41], t(849) = -4.24, p < .001; Std. beta = -0.31, 95% CI [-0.46, -0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(849) = -4.69, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.29, 95% CI [-1.82, -0.76], t(849) = -4.75, p < .001; Std. beta = -0.53, 95% CI [-0.75, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.15 (95% CI [2.83, 3.46], t(849) = 19.54, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(849) = 0.39, p = 0.695; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(849) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.03, 0.64], t(849) = 1.81, p = 0.070; Std. beta = 0.14, 95% CI [-0.01, 0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(849) = -2.67, p = 0.008; Std. beta = -0.31, 95% CI [-0.53, -0.08])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.35, -0.36], t(849) = -3.38, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.86 (95% CI [1.76, 1.96], t(849) = 36.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(849) = 0.62, p = 0.533; Std. beta = 0.07, 95% CI [-0.14, 0.27])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(849) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 9.94e-03, 95% CI [-0.09, 0.11], t(849) = 0.20, p = 0.838; Std. beta = 0.01, 95% CI [-0.13, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(849) = -4.26, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.10], t(849) = -3.30, p < .001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.87 (95% CI [2.75, 3.00], t(849) = 45.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.28], t(849) = 1.10, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(849) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.36, 95% CI [-0.49, -0.23], t(849) = -5.47, p < .001; Std. beta = -0.40, 95% CI [-0.55, -0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(849) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.41, 95% CI [-0.60, -0.22], t(849) = -4.17, p < .001; Std. beta = -0.46, 95% CI [-0.68, -0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.72 (95% CI [10.26, 11.19], t(849) = 45.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.37, 0.95], t(849) = 0.87, p = 0.386; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.31, 95% CI [-1.82, -0.81], t(849) = -5.09, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.32, 95% CI [-1.83, -0.80], t(849) = -4.97, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.86, 95% CI [-2.62, -1.10], t(849) = -4.78, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.22, -1.67], t(849) = -6.16, p < .001; Std. beta = -0.71, 95% CI [-0.93, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.30 (95% CI [1.19, 1.41], t(849) = 22.91, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(849) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(849) = -0.39, p = 0.693; Std. beta = -0.03, 95% CI [-0.17, 0.11])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(849) = 0.54, p = 0.589; Std. beta = 0.04, 95% CI [-0.11, 0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(849) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.22], t(849) = -4.49, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.28 (95% CI [1.17, 1.39], t(849) = 22.00, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(849) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(849) = -0.62, p = 0.537; Std. beta = -0.04, 95% CI [-0.18, 0.09])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.16, 0.06], t(849) = -0.91, p = 0.365; Std. beta = -0.06, 95% CI [-0.20, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.38e-03], t(849) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 3.02e-03])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.43, -0.10], t(849) = -3.20, p = 0.001; Std. beta = -0.34, 95% CI [-0.55, -0.13])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.33 (95% CI [1.22, 1.44], t(849) = 23.30, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(849) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(849) = 0.62, p = 0.538; Std. beta = 0.04, 95% CI [-0.09, 0.17])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.17, 0.04], t(849) = -1.19, p = 0.235; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(849) = -3.56, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.38, -0.07], t(849) = -2.79, p = 0.005; Std. beta = -0.28, 95% CI [-0.48, -0.08])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 46.33 (95% CI [45.04, 47.63], t(849) = 70.37, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(849) = -0.15, p = 0.881; Std. beta = -0.02, 95% CI [-0.22, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.29], t(849) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.78, 95% CI [-1.97, 0.41], t(849) = -1.29, p = 0.196; Std. beta = -0.09, 95% CI [-0.22, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.51], t(849) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.20, 95% CI [1.41, 4.99], t(849) = 3.50, p < .001; Std. beta = 0.36, 95% CI [0.16, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.90 (95% CI [38.39, 41.41], t(849) = 51.76, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(849) = 0.08, p = 0.938; Std. beta = 7.98e-03, 95% CI [-0.19, 0.21])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.55, 3.45], t(849) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
  • The effect of time point [3rd] is statistically significant and positive (beta = 2.27, 95% CI [0.78, 3.76], t(849) = 2.99, p = 0.003; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.37, 5.77], t(849) = 3.18, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.66, 95% CI [2.42, 6.90], t(849) = 4.08, p < .001; Std. beta = 0.44, 95% CI [0.23, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

isi

null

3

4,937.426

4,951.686

-2,465.713

4,931.426

isi

random

8

4,605.167

4,643.195

-2,294.584

4,589.167

342.259

5

0.000

who

null

3

4,671.034

4,685.294

-2,332.517

4,665.034

who

random

8

4,605.600

4,643.627

-2,294.800

4,589.600

75.434

5

0.000

phq

null

3

4,948.472

4,962.732

-2,471.236

4,942.472

phq

random

8

4,882.039

4,920.066

-2,433.019

4,866.039

76.433

5

0.000

gad

null

3

4,967.243

4,981.503

-2,480.621

4,961.243

gad

random

8

4,909.463

4,947.490

-2,446.731

4,893.463

67.780

5

0.000

wsas

null

3

6,134.981

6,149.242

-3,064.491

6,128.981

wsas

random

8

6,097.629

6,135.657

-3,040.815

6,081.629

47.352

5

0.000

shps_arousal

null

3

1,904.039

1,918.299

-949.019

1,898.039

shps_arousal

random

8

1,752.969

1,790.996

-868.484

1,736.969

161.070

5

0.000

shps_schedule

null

3

1,989.941

2,004.202

-991.971

1,983.941

shps_schedule

random

8

1,922.528

1,960.556

-953.264

1,906.528

77.413

5

0.000

shps_behavior

null

3

1,570.545

1,584.805

-782.272

1,564.545

shps_behavior

random

8

1,547.901

1,585.929

-765.951

1,531.901

32.644

5

0.000

shps_environment

null

3

1,858.024

1,872.284

-926.012

1,852.024

shps_environment

random

8

1,842.628

1,880.655

-913.314

1,826.628

25.397

5

0.000

dbas_consequence

null

3

3,452.521

3,466.782

-1,723.261

3,446.521

dbas_consequence

random

8

3,293.207

3,331.235

-1,638.604

3,277.207

169.314

5

0.000

dbas_worry

null

3

4,792.541

4,806.801

-2,393.271

4,786.541

dbas_worry

random

8

4,599.012

4,637.039

-2,291.506

4,583.012

203.529

5

0.000

dbas_expectation

null

3

3,786.392

3,800.652

-1,890.196

3,780.392

dbas_expectation

random

8

3,659.316

3,697.344

-1,821.658

3,643.316

137.076

5

0.000

dbas_medication

null

3

3,549.571

3,563.832

-1,771.786

3,543.571

dbas_medication

random

8

3,543.114

3,581.141

-1,763.557

3,527.114

16.458

5

0.006

psas_somatic

null

3

1,509.153

1,523.413

-751.576

1,503.153

psas_somatic

random

8

1,487.295

1,525.323

-735.648

1,471.295

31.858

5

0.000

psas_cognitive

null

3

2,069.165

2,083.426

-1,031.583

2,063.165

psas_cognitive

random

8

1,935.364

1,973.392

-959.682

1,919.364

143.801

5

0.000

psqi_global

null

3

4,450.346

4,464.606

-2,222.173

4,444.346

psqi_global

random

8

4,256.233

4,294.260

-2,120.116

4,240.233

204.113

5

0.000

mic_attention

null

3

1,742.574

1,756.834

-868.287

1,736.574

mic_attention

random

8

1,717.905

1,755.933

-850.953

1,701.905

34.669

5

0.000

mic_executive

null

3

1,741.834

1,756.094

-867.917

1,735.834

mic_executive

random

8

1,724.487

1,762.514

-854.243

1,708.487

27.347

5

0.000

mic_memory

null

3

1,676.383

1,690.643

-835.191

1,670.383

mic_memory

random

8

1,655.500

1,693.527

-819.750

1,639.500

30.883

5

0.000

nb_pcs

null

3

5,856.040

5,870.301

-2,925.020

5,850.040

nb_pcs

random

8

5,846.969

5,884.997

-2,915.485

5,830.969

19.071

5

0.002

nb_mcs

null

3

6,250.947

6,265.208

-3,122.474

6,244.947

nb_mcs

random

8

6,175.705

6,213.732

-3,079.852

6,159.705

85.242

5

0.000

Post hoc analysis

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

isi

1st

179

13.53 ± 3.82

179

13.40 ± 3.82

0.751

0.045

isi

2nd

148

11.07 ± 3.76

0.864

109

7.98 ± 3.67

1.906

0.000

1.087

isi

3rd

137

10.69 ± 3.72

0.999

105

7.57 ± 3.66

2.051

0.000

1.098

who

1st

179

9.82 ± 4.09

179

9.98 ± 4.09

0.708

-0.062

who

2nd

148

10.55 ± 3.96

-0.279

109

12.11 ± 3.80

-0.814

0.001

-0.597

who

3rd

137

10.75 ± 3.90

-0.353

105

12.54 ± 3.77

-0.977

0.000

-0.686

phq

1st

179

8.21 ± 5.07

179

8.80 ± 5.07

0.269

-0.204

phq

2nd

148

7.43 ± 4.86

0.268

109

6.29 ± 4.60

0.865

0.055

0.393

phq

3rd

137

7.57 ± 4.77

0.221

105

5.72 ± 4.56

1.061

0.002

0.636

gad

1st

179

7.54 ± 5.11

179

8.02 ± 5.11

0.374

-0.161

gad

2nd

148

7.10 ± 4.91

0.148

109

5.51 ± 4.65

0.844

0.008

0.535

gad

3rd

137

6.95 ± 4.82

0.197

105

5.05 ± 4.62

0.995

0.002

0.637

wsas

1st

179

16.77 ± 10.01

179

16.69 ± 10.01

0.937

0.014

wsas

2nd

148

15.95 ± 9.66

0.135

109

12.92 ± 9.20

0.621

0.011

0.500

wsas

3rd

137

16.65 ± 9.50

0.019

105

11.65 ± 9.14

0.829

0.000

0.823

shps_arousal

1st

179

3.02 ± 0.74

179

3.18 ± 0.74

0.039

-0.312

shps_arousal

2nd

148

2.83 ± 0.73

0.376

109

2.51 ± 0.70

1.292

0.001

0.604

shps_arousal

3rd

137

2.80 ± 0.72

0.421

105

2.40 ± 0.70

1.505

0.000

0.772

shps_schedule

1st

179

3.53 ± 0.89

179

3.58 ± 0.89

0.659

-0.079

shps_schedule

2nd

148

3.43 ± 0.86

0.192

109

3.13 ± 0.81

0.851

0.004

0.580

shps_schedule

3rd

137

3.40 ± 0.84

0.255

105

3.02 ± 0.81

1.064

0.000

0.730

shps_behavior

1st

179

1.99 ± 0.68

179

2.12 ± 0.68

0.067

-0.298

shps_behavior

2nd

148

2.01 ± 0.66

-0.055

109

1.90 ± 0.64

0.495

0.172

0.252

shps_behavior

3rd

137

2.00 ± 0.65

-0.027

105

1.79 ± 0.63

0.729

0.015

0.458

shps_environment

1st

179

2.33 ± 0.81

179

2.27 ± 0.81

0.469

0.118

shps_environment

2nd

148

2.27 ± 0.79

0.111

109

2.13 ± 0.76

0.274

0.129

0.281

shps_environment

3rd

137

2.27 ± 0.78

0.113

105

1.95 ± 0.75

0.608

0.001

0.612

dbas_consequence

1st

179

6.59 ± 1.88

179

6.64 ± 1.88

0.787

-0.043

dbas_consequence

2nd

148

6.25 ± 1.83

0.273

109

5.19 ± 1.76

1.173

0.000

0.857

dbas_consequence

3rd

137

5.93 ± 1.80

0.534

105

4.67 ± 1.75

1.596

0.000

1.018

dbas_worry

1st

179

14.20 ± 3.79

179

14.54 ± 3.79

0.396

-0.120

dbas_worry

2nd

148

12.97 ± 3.73

0.432

109

10.60 ± 3.65

1.384

0.000

0.832

dbas_worry

3rd

137

12.38 ± 3.70

0.640

105

9.83 ± 3.64

1.653

0.000

0.894

dbas_expectation

1st

179

7.17 ± 2.31

179

6.89 ± 2.31

0.243

0.185

dbas_expectation

2nd

148

6.83 ± 2.24

0.222

109

5.30 ± 2.16

1.031

0.000

0.994

dbas_expectation

3rd

137

6.41 ± 2.21

0.496

105

4.83 ± 2.15

1.333

0.000

1.021

dbas_medication

1st

179

3.15 ± 2.15

179

3.24 ± 2.15

0.695

-0.062

dbas_medication

2nd

148

3.51 ± 2.10

-0.254

109

2.94 ± 2.02

0.206

0.027

0.398

dbas_medication

3rd

137

3.45 ± 2.07

-0.212

105

2.69 ± 2.01

0.382

0.004

0.532

psas_somatic

1st

179

1.86 ± 0.68

179

1.91 ± 0.68

0.533

-0.108

psas_somatic

2nd

148

2.00 ± 0.65

-0.347

109

1.74 ± 0.62

0.393

0.001

0.632

psas_somatic

3rd

137

1.87 ± 0.64

-0.024

105

1.67 ± 0.62

0.560

0.016

0.476

psas_cognitive

1st

179

2.87 ± 0.85

179

2.97 ± 0.85

0.270

-0.177

psas_cognitive

2nd

148

2.67 ± 0.83

0.365

109

2.33 ± 0.79

1.141

0.001

0.599

psas_cognitive

3rd

137

2.51 ± 0.81

0.641

105

2.20 ± 0.79

1.376

0.003

0.558

psqi_global

1st

179

10.72 ± 3.17

179

11.01 ± 3.17

0.386

-0.128

psqi_global

2nd

148

9.41 ± 3.10

0.579

109

7.84 ± 3.02

1.399

0.000

0.692

psqi_global

3rd

137

9.41 ± 3.07

0.580

105

7.25 ± 3.01

1.657

0.000

0.949

mic_attention

1st

179

1.30 ± 0.76

179

1.42 ± 0.76

0.130

-0.250

mic_attention

2nd

148

1.28 ± 0.73

0.045

109

1.15 ± 0.70

0.555

0.165

0.260

mic_attention

3rd

137

1.33 ± 0.72

-0.063

105

1.07 ± 0.70

0.728

0.004

0.541

mic_executive

1st

179

1.28 ± 0.78

179

1.35 ± 0.78

0.415

-0.141

mic_executive

2nd

148

1.25 ± 0.75

0.071

109

1.15 ± 0.72

0.406

0.318

0.194

mic_executive

3rd

137

1.23 ± 0.74

0.107

105

1.03 ± 0.71

0.674

0.031

0.426

mic_memory

1st

179

1.33 ± 0.76

179

1.40 ± 0.76

0.417

-0.147

mic_memory

2nd

148

1.36 ± 0.74

-0.071

109

1.15 ± 0.70

0.548

0.020

0.471

mic_memory

3rd

137

1.27 ± 0.72

0.140

105

1.12 ± 0.69

0.635

0.091

0.348

nb_pcs

1st

179

46.33 ± 8.81

179

46.20 ± 8.81

0.882

0.027

nb_pcs

2nd

148

45.46 ± 8.47

0.169

109

48.08 ± 8.03

-0.366

0.012

-0.508

nb_pcs

3rd

137

45.55 ± 8.32

0.152

105

48.61 ± 7.97

-0.469

0.004

-0.594

nb_mcs

1st

179

39.90 ± 10.31

179

39.98 ± 10.31

0.938

-0.013

nb_mcs

2nd

148

41.90 ± 9.98

-0.310

109

45.56 ± 9.55

-0.861

0.003

-0.565

nb_mcs

3rd

137

42.17 ± 9.82

-0.351

105

46.91 ± 9.48

-1.071

0.000

-0.733

Between group

isi

1st

t(641.12) = -0.32, p = 0.751, Cohen d = 0.05, 95% CI (-0.92 to 0.66)

2st

t(756.67) = -6.61, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(773.70) = -6.53, p = 0.000, Cohen d = 1.10, 95% CI (-4.06 to -2.18)

who

1st

t(548.25) = 0.38, p = 0.708, Cohen d = -0.06, 95% CI (-0.69 to 1.01)

2st

t(687.05) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (0.60 to 2.52)

3rd

t(707.76) = 3.61, p = 0.000, Cohen d = -0.69, 95% CI (0.82 to 2.77)

phq

1st

t(500.72) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(636.28) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(656.77) = -3.06, p = 0.002, Cohen d = 0.64, 95% CI (-3.04 to -0.66)

gad

1st

t(506.80) = 0.89, p = 0.374, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(643.56) = -2.65, p = 0.008, Cohen d = 0.53, 95% CI (-2.77 to -0.41)

3rd

t(664.19) = -3.11, p = 0.002, Cohen d = 0.64, 95% CI (-3.10 to -0.70)

wsas

1st

t(522.87) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.62) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(682.47) = -4.15, p = 0.000, Cohen d = 0.82, 95% CI (-7.37 to -2.64)

shps_arousal

1st

t(600.04) = 2.07, p = 0.039, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(729.57) = -3.50, p = 0.001, Cohen d = 0.60, 95% CI (-0.49 to -0.14)

3rd

t(748.70) = -4.38, p = 0.000, Cohen d = 0.77, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(510.18) = 0.44, p = 0.659, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(647.50) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(668.20) = -3.59, p = 0.000, Cohen d = 0.73, 95% CI (-0.59 to -0.17)

shps_behavior

1st

t(556.74) = 1.84, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(694.81) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(715.36) = -2.45, p = 0.015, Cohen d = 0.46, 95% CI (-0.37 to -0.04)

shps_environment

1st

t(552.76) = -0.72, p = 0.469, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(691.21) = -1.52, p = 0.129, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(711.84) = -3.25, p = 0.001, Cohen d = 0.61, 95% CI (-0.52 to -0.13)

dbas_consequence

1st

t(560.71) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(698.32) = -4.69, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(718.78) = -5.47, p = 0.000, Cohen d = 1.02, 95% CI (-1.71 to -0.81)

dbas_worry

1st

t(648.31) = 0.85, p = 0.396, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(760.95) = -5.10, p = 0.000, Cohen d = 0.83, 95% CI (-3.29 to -1.46)

3rd

t(777.56) = -5.36, p = 0.000, Cohen d = 0.89, 95% CI (-3.48 to -1.61)

dbas_expectation

1st

t(571.26) = -1.17, p = 0.243, Cohen d = 0.18, 95% CI (-0.76 to 0.19)

2st

t(707.31) = -5.52, p = 0.000, Cohen d = 0.99, 95% CI (-2.08 to -0.99)

3rd

t(727.49) = -5.57, p = 0.000, Cohen d = 1.02, 95% CI (-2.13 to -1.02)

dbas_medication

1st

t(571.22) = 0.39, p = 0.695, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.27) = -2.21, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(727.46) = -2.90, p = 0.004, Cohen d = 0.53, 95% CI (-1.29 to -0.25)

psas_somatic

1st

t(525.96) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(664.91) = -3.25, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(685.77) = -2.41, p = 0.016, Cohen d = 0.48, 95% CI (-0.36 to -0.04)

psas_cognitive

1st

t(562.59) = 1.10, p = 0.270, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(699.96) = -3.28, p = 0.001, Cohen d = 0.60, 95% CI (-0.54 to -0.13)

3rd

t(720.38) = -3.01, p = 0.003, Cohen d = 0.56, 95% CI (-0.52 to -0.11)

psqi_global

1st

t(612.94) = 0.87, p = 0.386, Cohen d = -0.13, 95% CI (-0.37 to 0.95)

2st

t(738.61) = -4.07, p = 0.000, Cohen d = 0.69, 95% CI (-2.33 to -0.81)

3rd

t(757.15) = -5.47, p = 0.000, Cohen d = 0.95, 95% CI (-2.93 to -1.38)

mic_attention

1st

t(548.27) = 1.52, p = 0.130, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(687.07) = -1.39, p = 0.165, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(707.78) = -2.85, p = 0.004, Cohen d = 0.54, 95% CI (-0.44 to -0.08)

mic_executive

1st

t(526.25) = 0.82, p = 0.415, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(665.22) = -1.00, p = 0.318, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(686.07) = -2.16, p = 0.031, Cohen d = 0.43, 95% CI (-0.39 to -0.02)

mic_memory

1st

t(506.60) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.33) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(663.96) = -1.69, p = 0.091, Cohen d = 0.35, 95% CI (-0.33 to 0.02)

nb_pcs

1st

t(507.93) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(644.89) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(665.55) = 2.91, p = 0.004, Cohen d = -0.59, 95% CI (0.99 to 5.13)

nb_mcs

1st

t(538.06) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.06 to 2.23)

2st

t(677.26) = 2.98, p = 0.003, Cohen d = -0.56, 95% CI (1.24 to 6.07)

3rd

t(698.08) = 3.80, p = 0.000, Cohen d = -0.73, 95% CI (2.29 to 7.20)

Within treatment group

isi

1st vs 2st

t(595.81) = -14.89, p = 0.000, Cohen d = 1.91, 95% CI (-6.14 to -4.71)

1st vs 3rd

t(597.49) = -15.81, p = 0.000, Cohen d = 2.05, 95% CI (-6.56 to -5.11)

who

1st vs 2st

t(572.45) = 6.26, p = 0.000, Cohen d = -0.81, 95% CI (1.46 to 2.80)

1st vs 3rd

t(573.19) = 7.41, p = 0.000, Cohen d = -0.98, 95% CI (1.88 to 3.24)

phq

1st vs 2st

t(557.92) = -6.60, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(558.30) = -7.98, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.33)

gad

1st vs 2st

t(559.90) = -6.44, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(560.32) = -7.49, p = 0.000, Cohen d = 1.00, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(564.96) = -4.75, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.21)

1st vs 3rd

t(565.49) = -6.26, p = 0.000, Cohen d = 0.83, 95% CI (-6.62 to -3.45)

shps_arousal

1st vs 2st

t(586.16) = -10.03, p = 0.000, Cohen d = 1.29, 95% CI (-0.80 to -0.54)

1st vs 3rd

t(587.38) = -11.52, p = 0.000, Cohen d = 1.51, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(560.99) = -6.50, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(561.43) = -8.02, p = 0.000, Cohen d = 1.06, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(574.84) = -3.81, p = 0.000, Cohen d = 0.49, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(575.64) = -5.54, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(573.73) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(574.50) = -4.61, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas_consequence

1st vs 2st

t(575.93) = -9.05, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(576.77) = -12.14, p = 0.000, Cohen d = 1.60, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(597.41) = -10.82, p = 0.000, Cohen d = 1.38, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(599.18) = -12.76, p = 0.000, Cohen d = 1.65, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(578.78) = -7.97, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(579.72) = -10.15, p = 0.000, Cohen d = 1.33, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(578.77) = -1.59, p = 0.223, Cohen d = 0.21, 95% CI (-0.66 to 0.07)

1st vs 3rd

t(579.71) = -2.91, p = 0.007, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas_somatic

1st vs 2st

t(565.90) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(566.46) = -4.23, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(576.45) = -8.80, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(577.30) = -10.47, p = 0.000, Cohen d = 1.38, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(589.30) = -10.88, p = 0.000, Cohen d = 1.40, 95% CI (-3.75 to -2.60)

1st vs 3rd

t(590.65) = -12.71, p = 0.000, Cohen d = 1.66, 95% CI (-4.34 to -3.18)

mic_attention

1st vs 2st

t(572.46) = -4.27, p = 0.000, Cohen d = 0.55, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(573.19) = -5.52, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(565.99) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(566.55) = -5.09, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(559.84) = -4.18, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(560.25) = -4.78, p = 0.000, Cohen d = 0.63, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(560.27) = 2.80, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.21)

1st vs 3rd

t(560.69) = 3.53, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(569.51) = 6.61, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(570.16) = 8.11, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(542.71) = -7.61, p = 0.000, Cohen d = 0.86, 95% CI (-3.09 to -1.82)

1st vs 3rd

t(548.52) = -8.57, p = 0.000, Cohen d = 1.00, 95% CI (-3.49 to -2.19)

who

1st vs 2st

t(530.54) = 2.44, p = 0.030, Cohen d = -0.28, 95% CI (0.14 to 1.32)

1st vs 3rd

t(534.13) = 3.01, p = 0.005, Cohen d = -0.35, 95% CI (0.32 to 1.53)

phq

1st vs 2st

t(523.50) = -2.34, p = 0.039, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(526.07) = -1.87, p = 0.123, Cohen d = 0.22, 95% CI (-1.32 to 0.03)

gad

1st vs 2st

t(524.45) = -1.29, p = 0.396, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(527.14) = -1.67, p = 0.191, Cohen d = 0.20, 95% CI (-1.27 to 0.10)

wsas

1st vs 2st

t(526.87) = -1.18, p = 0.478, Cohen d = 0.13, 95% CI (-2.18 to 0.55)

1st vs 3rd

t(529.91) = -0.16, p = 1.000, Cohen d = 0.02, 95% CI (-1.52 to 1.29)

shps_arousal

1st vs 2st

t(537.52) = -3.30, p = 0.002, Cohen d = 0.38, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(542.30) = -3.60, p = 0.001, Cohen d = 0.42, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(524.96) = -1.68, p = 0.188, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(527.73) = -2.16, p = 0.062, Cohen d = 0.25, 95% CI (-0.25 to -0.01)

shps_behavior

1st vs 2st

t(531.73) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.12)

1st vs 3rd

t(535.51) = 0.23, p = 1.000, Cohen d = -0.03, 95% CI (-0.09 to 0.11)

shps_environment

1st vs 2st

t(531.17) = -0.97, p = 0.662, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(534.86) = -0.97, p = 0.668, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

dbas_consequence

1st vs 2st

t(532.27) = -2.39, p = 0.034, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(536.14) = -4.55, p = 0.000, Cohen d = 0.53, 95% CI (-0.94 to -0.37)

dbas_worry

1st vs 2st

t(543.60) = -3.81, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.60)

1st vs 3rd

t(549.60) = -5.50, p = 0.000, Cohen d = 0.64, 95% CI (-2.48 to -1.17)

dbas_expectation

1st vs 2st

t(533.71) = -1.95, p = 0.103, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(537.82) = -4.24, p = 0.000, Cohen d = 0.50, 95% CI (-1.12 to -0.41)

dbas_medication

1st vs 2st

t(533.71) = 2.23, p = 0.052, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(537.82) = 1.81, p = 0.142, Cohen d = -0.21, 95% CI (-0.03 to 0.64)

psas_somatic

1st vs 2st

t(527.33) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(530.43) = 0.20, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.11)

psas_cognitive

1st vs 2st

t(532.53) = -3.20, p = 0.003, Cohen d = 0.37, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(536.44) = -5.47, p = 0.000, Cohen d = 0.64, 95% CI (-0.49 to -0.23)

psqi_global

1st vs 2st

t(539.17) = -5.09, p = 0.000, Cohen d = 0.58, 95% CI (-1.82 to -0.81)

1st vs 3rd

t(544.27) = -4.96, p = 0.000, Cohen d = 0.58, 95% CI (-1.84 to -0.79)

mic_attention

1st vs 2st

t(530.54) = -0.39, p = 1.000, Cohen d = 0.05, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(534.13) = 0.54, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.14)

mic_executive

1st vs 2st

t(527.37) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(530.48) = -0.91, p = 0.730, Cohen d = 0.11, 95% CI (-0.16 to 0.06)

mic_memory

1st vs 2st

t(524.42) = 0.62, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(527.11) = -1.19, p = 0.471, Cohen d = 0.14, 95% CI (-0.17 to 0.04)

nb_pcs

1st vs 2st

t(524.62) = -1.48, p = 0.281, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(527.34) = -1.29, p = 0.394, Cohen d = 0.15, 95% CI (-1.98 to 0.41)

nb_mcs

1st vs 2st

t(529.09) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.46)

1st vs 3rd

t(532.45) = 2.99, p = 0.006, Cohen d = -0.35, 95% CI (0.78 to 3.76)

Plot

Clinical significance

T1

T2

T3

outcome

control1

treatment1

p-value2

control1

treatment1

p-value2

control1

treatment1

p-value2

isi

89%

85%

0.206

61%

31%

0.000

56%

29%

0.000

psqi

96%

97%

0.586

89%

74%

0.003

89%

65%

0.000

phq

31%

38%

0.148

32%

19%

0.019

30%

18%

0.035

gad

30%

33%

0.494

26%

17%

0.061

27%

16%

0.045

wsas

74%

72%

0.721

68%

55%

0.041

69%

49%

0.001

1%

2Pearson's Chi-squared test